SNP Calling Research Articles

Lessons learned from implementing a national infrastructure in Sweden for storage and analysis of next-generation sequencing data

As sequencing get cheaper more and more researchers turn to this technology for answers to their questions. The large amounts of generated data will have to be stored somewhere, and the tools to analyse it will have to be updated constantly. UPPNEX tries to solve these problems through high performance computing, large scale and high availability storage, an extensive bioinformatics software suite, up-to-date reference genomes and annotations and a support function with systems and application experts. The software maintained on the computers are mostly the popular open source applications used by the international bioinformatics community, and comprises functions such as alignment, de novo assembly, SNP calling, methylation and RNAseq analysis. There are over 300 separate projects at UPPNEX which all belong to different research projects, such as the sequencing of the flycatcher and Norwegian spruce. The focus of the poster is on the technical and organizational implementations at UPPNEX to handle its task.

Development of strategies for SNP detection in RNA-seq data: application to lymphoblastoid cell lines and evaluation using 1000 Genomes data.

Next-generation RNA sequencing (RNA-seq) maps and analyzes transcriptomes and generates data on sequence variation in expressed genes. There are few reported studies on analysis strategies to maximize the yield of quality RNA-seq SNP data. We evaluated the performance of different SNP-calling methods following alignment to both genome and transcriptome by applying them to RNA-seq data from a HapMap lymphoblastoid cell line sample and comparing results with sequence variation data from 1000 Genomes. We determined that the best method to achieve high specificity and sensitivity, and greatest number of SNP calls, is to remove duplicate sequence reads after alignment to the genome and to call SNPs using SAMtools. The accuracy of SNP calls is dependent on sequence coverage available. In terms of specificity, 89% of RNA-seq SNPs calls were true variants where coverage is >10X. In terms of sensitivity, at >10X coverage 92% of all expected SNPs in expressed exons could be detected. Overall, the results indicate that RNA-seq SNP data are a very useful by-product of sequence-based transcriptome analysis. If RNA-seq is applied to disease tissue samples and assuming that genes carrying mutations relevant to disease biology are being expressed, a very high proportion of these mutations can be detected.

Exome sequencing of tumors: relevance in copy-number alteration (CNA) analysis and fixed tissue samples.

CNA calling with the cryopreserved exome data yields good results, which were compared with CGH data for validation. All CNA regions which were previously detected with CGH analysis were correctly detected with the exome data. SNP calling was performed on all samples, more than 90% of the SNPs found in FFPE exomes were also found in the corresponding cryo exomes. A lower target coverage in FFPE yields a lower number of detected SNPs, for both samples, but FFPE-related degradations do not seem to cause false positives, in either samples.

A dynamic Bayesian Markov model for phasing and characterizing haplotypes in next-generation sequencing

Next-generation sequencing (NGS) technologies have enabled whole-genome discovery and analysis of genetic variants in many species of interest. Individuals are often sequenced at low coverage for detecting novel variants, phasing haplotypes and inferring population structures. Although several tools have been developed for SNP and genotype calling in NGS data, haplotype phasing is often done separately on the called genotypes. We propose a dynamic Bayesian Markov model (DBM) for simultaneous genotype calling and haplotype phasing in low-coverage NGS data of unrelated individuals. Our method is fully probabilistic that produces consistent inference of genotypes, haplotypes and recombination probabilities. Using data from the 1000 Genomes Project, we demonstrate that DBM not only yields more accurate results than some popular methods, but also provides novel characterization of haplotype structures at the individual level for visualization, interpretation and comparison in downstream analysis. DBM is a powerful and flexible tool that can be applied to many sequencing studies. Its statistical framework can also be extended to accommodate broader scopes of data. http://stat.psu.edu/∼yuzhang/software/dbm.tar. Supplementary data are available at Bioinformatics online.

AMY-tree: an algorithm to use whole genome SNP calling for Y chromosomal phylogenetic applications

BackgroundDue to the rapid progress of next-generation sequencing (NGS) facilities, an explosion of human whole genome data will become available in the coming years. These data can be used to optimize and to increase the resolution of the phylogenetic Y chromosomal tree. Moreover, the exponential growth of known Y chromosomal lineages will require an automatic determination of the phylogenetic position of an individual based on whole genome SNP calling data and an up to date Y chromosomal tree.ResultsWe present an automated approach, ‘AMY-tree’, which is able to determine the phylogenetic position of a Y chromosome using a whole genome SNP profile, independently from the NGS platform and SNP calling program, whereby mistakes in the SNP calling or phylogenetic Y chromosomal tree are taken into account. Moreover, AMY-tree indicates ambiguities within the present phylogenetic tree and points out new Y-SNPs which may be phylogenetically relevant. The AMY-tree software package was validated successfully on 118 whole genome SNP profiles of 109 males with different origins. Moreover, support was found for an unknown recurrent mutation, wrong reported mutation conversions and a large amount of new interesting Y-SNPs.ConclusionsTherefore, AMY-tree is a useful tool to determine the Y lineage of a sample based on SNP calling, to identify Y-SNPs with yet unknown phylogenetic position and to optimize the Y chromosomal phylogenetic tree in the future. AMY-tree will not add lineages to the existing phylogenetic tree of the Y-chromosome but it is the first step to analyse whole genome SNP profiles in a phylogenetic framework.

An Improved Genotyping by Sequencing (GBS) Approach Offering Increased Versatility and Efficiency of SNP Discovery and Genotyping

Highly parallel SNP genotyping platforms have been developed for some important crop species, but these platforms typically carry a high cost per sample for first-time or small-scale users. In contrast, recently developed genotyping by sequencing (GBS) approaches offer a highly cost effective alternative for simultaneous SNP discovery and genotyping. In the present investigation, we have explored the use of GBS in soybean. In addition to developing a novel analysis pipeline to call SNPs and indels from the resulting sequence reads, we have devised a modified library preparation protocol to alter the degree of complexity reduction. We used a set of eight diverse soybean genotypes to conduct a pilot scale test of the protocol and pipeline. Using ApeKI for GBS library preparation and sequencing on an Illumina GAIIx machine, we obtained 5.5 M reads and these were processed using our pipeline. A total of 10,120 high quality SNPs were obtained and the distribution of these SNPs mirrored closely the distribution of gene-rich regions in the soybean genome. A total of 39.5% of the SNPs were present in genic regions and 52.5% of these were located in the coding sequence. Validation of over 400 genotypes at a set of randomly selected SNPs using Sanger sequencing showed a 98% success rate. We then explored the use of selective primers to achieve a greater complexity reduction during GBS library preparation. The number of SNP calls could be increased by almost 40% and their depth of coverage was more than doubled, thus opening the door to an increase in the throughput and a significant decrease in the per sample cost. The approach to obtain high quality SNPs developed here will be helpful for marker assisted genomics as well as assessment of available genetic resources for effective utilisation in a wide number of species.

A unified approach for allele frequency estimation, SNP detection and association studies based on pooled sequencing data using EM algorithms

BackgroundGenome-wide association studies (GWAS) have identified many common polymorphisms associated with complex traits. However, these associated common variants explain only a small fraction of the phenotypic variances, leaving a substantial portion of genetic heritability unexplained. As a result, searches for "missing" heritability are drawing increasing attention, particularly for rare variant studies that often require a large sample size and, thus, extensive sequencing effort. Although the development of next generation sequencing (NGS) technologies has made it possible to sequence a large number of reads economically and efficiently, it is still often cost prohibitive to sequence thousands of individuals that are generally required for association studies. A more efficient and cost-effective design would involve pooling the genetic materials of multiple individuals together and then sequencing the pools, instead of the individuals. This pooled sequencing approach has improved the plausibility of association studies for rare variants, while, at the same time, posed a great challenge to the pooled sequencing data analysis, essentially because individual sample identity is lost, and NGS sequencing errors could be hard to distinguish from low frequency alleles.ResultsA unified approach for estimating minor allele frequency, SNP calling and association studies based on pooled sequencing data using an expectation maximization (EM) algorithm is developed in this paper. This approach makes it possible to study the effects of minor allele frequency, sequencing error rate, number of pools, number of individuals in each pool, and the sequencing depth on the estimation accuracy of minor allele frequencies. We show that the naive method of estimating minor allele frequencies by taking the fraction of observed minor alleles can be significantly biased, especially for rare variants. In contrast, our EM approach can give an unbiased estimate of the minor allele frequency under all scenarios studied in this paper. A SNP calling approach, EM-SNP, for pooled sequencing data based on the EM algorithm is then developed and compared with another recent SNP calling method, SNVer. We show that EM-SNP outperforms SNVer in terms of the fraction of db-SNPs among the called SNPs, as well as transition/transversion (Ti/Tv) ratio. Finally, the EM approach is used to study the association between variants and type I diabetes.ConclusionsThe EM-based approach for the analysis of pooled sequencing data can accurately estimate minor allele frequencies, call SNPs, and find associations between variants and complex traits. This approach is especially useful for studies involving rare variants.

Multiplex DNA amplification and barcoding in a single reaction for 454 Roche sequencing: A comprehensive study on the control region of the mitochondrial genome

The analysis of the non-coding region of the mitochondrial genome using Sanger sequencing remains a laborious and time-consuming assay with too low resolution for the identification of low-frequency heteroplasmy or for mixture interpretation. In this study, an experimental design was tested in which the complete hypervariable region of the mitochondrial genome was sequenced using a novel barcoding strategy. The strategy involves a single-step multiplex nested PCR and we demonstrate its effectiveness by sequencing two multiplex reactions of two amplicons each covering the complete hypervariable region of the mitochondrial genome for 58 reference samples, 30 of which were analysed in triplicate, and 10 casework samples, each analysed in triplicate, on a 454 Roche DNA pyrosequencer with GS FLX chemistry using Multiplex Identifier (MID) primers to discriminate between samples. The generated reads for forensic (±3600 reads/MID) and reference samples (±466 reads/MID) allowed us to evaluate the accuracy in SNP calling and the variation in heteroplasmy and sequencing error rates in homopolymeric stretches between replicates.

SNP Design from 454 Sequencing of Podosphaera plantaginis Transcriptome Reveals a Genetically Diverse Pathogen Metapopulation with High Levels of Mixed-Genotype Infection

BackgroundMolecular tools may greatly improve our understanding of pathogen evolution and epidemiology but technical constraints have hindered the development of genetic resources for parasites compared to free-living organisms. This study aims at developing molecular tools for Podosphaera plantaginis, an obligate fungal pathogen of Plantago lanceolata. This interaction has been intensively studied in the Åland archipelago of Finland with epidemiological data collected from over 4,000 host populations annually since year 2001.Principal FindingsA cDNA library of a pooled sample of fungal conidia was sequenced on the 454 GS-FLX platform. Over 549,411 reads were obtained and annotated into 45,245 contigs. Annotation data was acquired for 65.2% of the assembled sequences. The transcriptome assembly was screened for SNP loci, as well as for functionally important genes (mating-type genes and potential effector proteins). A genotyping assay of 27 SNP loci was designed and tested on 380 infected leaf samples from 80 populations within the Åland archipelago. With this panel we identified 85 multilocus genotypes (MLG) with uneven frequencies across the pathogen metapopulation. Approximately half of the sampled populations contain polymorphism. Our genotyping protocol revealed mixed-genotype infection within a single host leaf to be common. Mixed infection has been proposed as one of the main drivers of pathogen evolution, and hence may be an important process in this pathosystem.SignificanceThe developed SNP panel offers exciting research perspectives for future studies in this well-characterized pathosystem. Also, the transcriptome provides an invaluable novel genomic resource for powdery mildews, which cause significant yield losses on commercially important crops annually. Furthermore, the features that render genetic studies in this system a challenge are shared with the majority of obligate parasitic species, and hence our results provide methodological insights from SNP calling to field sampling protocols for a wide range of biological systems.

Steps to ensure accuracy in genotype and SNP calling from Illumina sequencing data

BackgroundAccurate calling of SNPs and genotypes from next-generation sequencing data is an essential prerequisite for most human genetics studies. A number of computational steps are required or recommended when translating the raw sequencing data into the final calls. However, whether each step does contribute to the performance of variant calling and how it affects the accuracy still remain unclear, making it difficult to select and arrange appropriate steps to derive high quality variants from different sequencing data. In this study, we made a systematic assessment of the relative contribution of each step to the accuracy of variant calling from Illumina DNA sequencing data.ResultsWe found that the read preprocessing step did not improve the accuracy of variant calling, contrary to the general expectation. Although trimming off low-quality tails helped align more reads, it introduced lots of false positives. The ability of markup duplication, local realignment and recalibration, to help eliminate false positive variants depended on the sequencing depth. Rearranging these steps did not affect the results. The relative performance of three popular multi-sample SNP callers, SAMtools, GATK, and GlfMultiples, also varied with the sequencing depth.ConclusionsOur findings clarify the necessity and effectiveness of computational steps for improving the accuracy of SNP and genotype calls from Illumina sequencing data and can serve as a general guideline for choosing SNP calling strategies for data with different coverage.

Detection of FLT3 Internal Tandem Duplication in Targeted, Short-Read-Length, Next-Generation Sequencing Data

A recurrent somatic mutation frequently found in cytogenetically normal acute myeloid leukemia (AML) is internal tandem duplication (ITD) in the fms-related tyrosine kinase 3 gene (FLT3). This mutation is generally detected in the clinical laboratory by PCR and electrophoresis-based product sizing. As the number of clinically relevant somatic mutations in AML increases, it becomes increasingly attractive to incorporate FLT3 ITD testing into multiplex assays for many somatic mutations simultaneously, using next-generation sequencing (NGS). However, the performance of most NGS analysis tools for identifying medium-size insertions such as FLT3 ITD mutations is largely unknown. We used a multigene, targeted NGS assay to obtain deep sequence coverage (>1000-fold) of FLT3 and 26 other genes from 22 FLT3 ITD-positive and 29 ITD-negative specimens to examine the performance of several commonly used NGS analysis tools for identifying FLT3 ITD mutations. ITD mutations were present in hybridization-capture sequencing data, and Pindel was the only tool out of the seven tested that reliably detected these insertions. Pindel had 100% sensitivity (95% CI = 83% to 100%) and 100% specificity (95% CI = 88% to 100%) in our samples; Pindel provided accurate ITD insertion sizes and was able to detect ITD alleles present at estimated frequencies as low as 1%. These data demonstrate that FLT3 ITDs can be reliably detected in panel-based, next-generation sequencing assays.

Library Preparation and Multiplex Capture for Massive Parallel Sequencing Applications Made Efficient and Easy

During the recent years, rapid development of sequencing technologies and a competitive market has enabled researchers to perform massive sequencing projects at a reasonable cost. As the price for the actual sequencing reactions drops, enabling more samples to be sequenced, the relative price for preparing libraries gets larger and the practical laboratory work becomes complex and tedious. We present a cost-effective strategy for simplified library preparation compatible with both whole genome- and targeted sequencing experiments. An optimized enzyme composition and reaction buffer reduces the number of required clean-up steps and allows for usage of bulk enzymes which makes the whole process cheap, efficient and simple. We also present a two-tagging strategy, which allows for multiplex sequencing of targeted regions. To prove our concept, we have prepared libraries for low-pass sequencing from 100 ng DNA, performed 2-, 4- and 8-plex exome capture and a 96-plex capture of a 500 kb region. In all samples we see a high concordance (>99.4%) of SNP calls when comparing to commercially available SNP-chip platforms.

SNP calling by sequencing pooled samples

BackgroundPerforming high throughput sequencing on samples pooled from different individuals is a strategy to characterize genetic variability at a small fraction of the cost required for individual sequencing. In certain circumstances some variability estimators have even lower variance than those obtained with individual sequencing. SNP calling and estimating the frequency of the minor allele from pooled samples, though, is a subtle exercise for at least three reasons. First, sequencing errors may have a much larger relevance than in individual SNP calling: while their impact in individual sequencing can be reduced by setting a restriction on a minimum number of reads per allele, this would have a strong and undesired effect in pools because it is unlikely that alleles at low frequency in the pool will be read many times. Second, the prior allele frequency for heterozygous sites in individuals is usually 0.5 (assuming one is not analyzing sequences coming from, e.g. cancer tissues), but this is not true in pools: in fact, under the standard neutral model, singletons (i.e. alleles of minimum frequency) are the most common class of variants because P(f) ∝ 1/f and they occur more often as the sample size increases. Third, an allele appearing only once in the reads from a pool does not necessarily correspond to a singleton in the set of individuals making up the pool, and vice versa, there can be more than one read – or, more likely, none – from a true singleton.ResultsTo improve upon existing theory and software packages, we have developed a Bayesian approach for minor allele frequency (MAF) computation and SNP calling in pools (and implemented it in a program called snape): the approach takes into account sequencing errors and allows users to choose different priors. We also set up a pipeline which can simulate the coalescence process giving rise to the SNPs, the pooling procedure and the sequencing. We used it to compare the performance of snape to that of other packages.ConclusionsWe present a software which helps in calling SNPs in pooled samples: it has good power while retaining a low false discovery rate (FDR). The method also provides the posterior probability that a SNP is segregating and the full posterior distribution of f for every SNP. In order to test the behaviour of our software, we generated (through simulated coalescence) artificial genomes and computed the effect of a pooled sequencing protocol, followed by SNP calling. In this setting, snape has better power and False Discovery Rate (FDR) than the comparable packages samtools, PoPoolation, Varscan : for N = 50 chromosomes, snape has power ≈ 35%and FDR ≈ 2.5%. snape is available at http://code.google.com/p/snape-pooled/ (source code and precompiled binaries).

Genotyping in the cloud with Crossbow.

Crossbow is a scalable, portable, and automatic cloud computing tool for identifying SNPs from high-coverage, short-read resequencing data. It is built on Apache Hadoop, an implementation of the MapReduce software framework. Hadoop allows Crossbow to distribute read alignment and SNP calling subtasks over a cluster of commodity computers. Two robust tools, Bowtie and SOAPsnp, implement the fundamental alignment and variant calling operations respectively, and have demonstrated capabilities within Crossbow of analyzing approximately one billion short reads per hour on a commodity Hadoop cluster with 320 cores. Through protocol examples, this unit will demonstrate the use of Crossbow for identifying variations in three different operating modes: on a Hadoop cluster, on a single computer, and on the Amazon Elastic MapReduce cloud computing service.

G.O.4 Search for SNPs modifiers in DMD with different corticosteroids response by candidate genes targeted resequencing

<h2>Abstract</h2> Corticosteroid treatment is an established therapy for DMD patients, with proven efficacy in delaying the loss of the ambulation. However the response to corticosteroids is not the same for all DMD individuals, some patients having no appreciable benefit. Considering the considerable side effects of this therapy, it would be desirable to identify biomarkers able to predict the response to corticosteroids. We have carried out a targeted exon sequencing in 243 genes (identified within the BIO-NMD consortium) which are connected to DMD pathways (identified using the MedScan informatics tools developed by ARIADNE), preceded by a targeted specific enrichment step. For target enrichment Agilent's SureSelect in solution approach was selected. Sixteen DMD patients (eight high responders and eight low responders) were processed using 5500xl SOLiD™ Sequencer. Quality filtering was carried out on the SNP calls returned by LifeScope using quality filters, SNPs in introns were discarded. After quality filtering, 735 SNPs were identified across all samples. We applied two novel statistical methods for analysing variance in the two DMD groups: the multidimensional scaling, and the discriminant analysis, both used to determine which variables discriminate between two (or more) naturally occurring groups. Using both tools we were able to identify 34 SNPs which are differently represented in the two categories (responders/low responders) and do discriminate patients. These SNPs belong to 23 genes, several of which encode for the structural proteins vinculins, integrins, and laminins. In order to validate this data, and to confer robustness and specificity to our identified SNPs, 84 additional DMD patients as well as other non-DMD individuals are now being studied to increase the statistical power of the data. Validated SNPs may represent pharmacogenetic biomarkers for corticosteroid therapy response.

Genome-Wide Somatic Copy Number Alterations in Low-Grade PanINs and IPMNs from Individuals with a Family History of Pancreatic Cancer

Characterizing the earliest chromosomal alterations of pancreatic precursor neoplasms from individuals with a familial aggregation of pancreatic cancer may provide clues as to the loci of pancreatic cancer susceptibility genes. We used Illumina 370/660K SNP arrays to conduct genome-wide copy number analysis in 60 benign neoplasms [58 mostly low-grade pancreatic intraepithelial neoplasias (PanIN) and intraductal papillary mucinous neoplasms (IPMN) and two pancreatic neuroendocrine tumors (PNET)] and matched normal tissues from 16 individuals with a family history of pancreatic cancer. PanINs and IPMNs were analyzed for KRAS codon 12/13 mutations. Of 40 benign neoplasms with adequate SNP calls and allele ratios, somatic chromosomal copy number changes were identifiable in only nine lesions, including eight of the 38 PanIN/IPMNs (two of which had identical alterations) and one of the two PNETs. Only two precursor lesions had more than one somatic copy number alteration. In contrast, the overwhelming majority (∼95%) of PanINs harbored KRAS mutations. The chromosomal alterations identified included nine chromosomal arms affected by chromosomal loss and two by chromosomal gain. Copy number loss spanning 9p21.3 was identified in three precursor lesions; two precursors had chromosomal losses affecting 6q and 17p. Low- and intermediate-grade PanINs and IPMNs from patients with a family history of pancreatic cancer harbor few if any somatic chromosomal alterations. The absence of a locus of recurrent chromosomal loss in most low-grade pancreatic cancer precursor lesions supports the hypothesis that there is no one tumor suppressor gene locus consistently involved in initiating familial pancreatic neoplasia.

SNP calling, genotype calling, and sample allele frequency estimation from New-Generation Sequencing data.

We present a statistical framework for estimation and application of sample allele frequency spectra from New-Generation Sequencing (NGS) data. In this method, we first estimate the allele frequency spectrum using maximum likelihood. In contrast to previous methods, the likelihood function is calculated using a dynamic programming algorithm and numerically optimized using analytical derivatives. We then use a Bayesian method for estimating the sample allele frequency in a single site, and show how the method can be used for genotype calling and SNP calling. We also show how the method can be extended to various other cases including cases with deviations from Hardy-Weinberg equilibrium. We evaluate the statistical properties of the methods using simulations and by application to a real data set.

High-throughput genotyping in citrus accessions using an SNP genotyping array

We developed a 384 multiplexed SNP array, named CitSGA-1, for the genotyping of Citrus cultivars, and evaluated the performance and reliability of the genotyping. SNPs were surveyed by direct sequence comparison of the sequence tagged site (STS) fragment amplified from genomic DNA of cultivars representing the genetic diversity of citrus breeding in Japan. Among 1497 SNPs candidates, 384 SNPs for a high-throughput genotyping array were selected based on physical parameters of Illumina’s bead array criteria. The assay using CitSGA-1 was applied to a hybrid population of 88 progeny and 103 citrus accessions for breeding in Japan, which resulted in 73,726 SNP calls. A total of 351 SNPs (91 %) could call different genotypes among the DNA samples, resulting in a success rate for the assay comparable to previously reported rates for other plant species. To confirm the reliability of SNP genotype calls, parentage analysis was applied, and it indicated that the number of reliable SNPs and corresponding STSs were 276 and 213, respectively. The multiplexed SNP genotyping array reported here will be useful for the efficient construction of linkage map, for the detection of markers for marker-assisted breeding, and for the identification of cultivars.

AdapterRemoval: easy cleaning of next-generation sequencing reads.

BackgroundWith the advent of next-generation sequencing there is an increased demand for tools to pre-process and handle the vast amounts of data generated. One recurring problem is adapter contamination in the reads, i.e. the partial or complete sequencing of adapter sequences. These adapter sequences have to be removed as they can hinder correct mapping of the reads and influence SNP calling and other downstream analyses.FindingsWe present a tool called AdapterRemoval which is able to pre-process both single and paired-end data. The program locates and removes adapter residues from the reads, it is able to combine paired reads if they overlap, and it can optionally trim low-quality nucleotides. Furthermore, it can look for adapter sequence in both the 5’ and 3’ ends of the reads. This is a flexible tool that can be tuned to accommodate different experimental settings and sequencing platforms producing FASTQ files. AdapterRemoval is shown to be good at trimming adapters from both single-end and paired-end data.ConclusionsAdapterRemoval is a comprehensive tool for analyzing next-generation sequencing data. It exhibits good performance both in terms of sensitivity and specificity. AdapterRemoval has already been used in various large projects and it is possible to extend it further to accommodate application-specific biases in the data.

Reference-free SNP calling: improved accuracy by preventing incorrect calls from repetitive genomic regions

BackgroundSingle nucleotide polymorphisms (SNPs) are the most abundant type of genetic variation in eukaryotic genomes and have recently become the marker of choice in a wide variety of ecological and evolutionary studies. The advent of next-generation sequencing (NGS) technologies has made it possible to efficiently genotype a large number of SNPs in the non-model organisms with no or limited genomic resources. Most NGS-based genotyping methods require a reference genome to perform accurate SNP calling. Little effort, however, has yet been devoted to developing or improving algorithms for accurate SNP calling in the absence of a reference genome.ResultsHere we describe an improved maximum likelihood (ML) algorithm called iML, which can achieve high genotyping accuracy for SNP calling in the non-model organisms without a reference genome. The iML algorithm incorporates the mixed Poisson/normal model to detect composite read clusters and can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions. Through analysis of simulation and real sequencing datasets, we demonstrate that in comparison with ML or a threshold approach, iML can remarkably improve the accuracy of de novo SNP genotyping and is especially powerful for the reference-free genotyping in diploid genomes with high repeat contents.ConclusionsThe iML algorithm can efficiently prevent incorrect SNP calls resulting from repetitive genomic regions, and thus outperforms the original ML algorithm by achieving much higher genotyping accuracy. Our algorithm is therefore very useful for accurate de novo SNP genotyping in the non-model organisms without a reference genome.ReviewersThis article was reviewed by Dr. Richard Durbin, Dr. Liliana Florea (nominated by Dr. Steven Salzberg) and Dr. Arcady Mushegian.